facebookresearch / Hgnn
Programming Languages
Hyperbolic Graph Neural Networks
Requirements
- Python 3.7
- PyTorch >= 1.1
- RDKit
- numpy
- networkx
- scikit-learn
A recipe about installing the requirements is provided in install.sh
.
Data Preprocess
For the Ethereum dataset, go to data/ethereum
and run
download_ethereum.sh
For the node classification dataset, go to data/node
and run
download_node.sh
For QM8, QM9 and ZINC, go to data/qm8
, data/qm9
and data/zinc
, respectively and run
python get_data.py
For the synthetic dataset, go to data/synthetic
and run
python generate_graphs.py
For TU Dortmund datasets, go to data/tu
and run
python data_preprocess.py {REDDIT-MULTI-12K, PROTEINS_full, ENZYMES, DD, COLLAB}
Run Experiments
The code can be run on SLURM and on multiple GPUs. To run on multi GPUs, use
python -m torch.distributed.launch --nproc_per_node=NUM_GPU main.py --task {qm8, qm9, zinc, ethereum, node_classification, synthetic, dd, enzymes, proteins, reddit, collab}
Inputs of Riemannian GNN
Here we introduce the inputs of Riemannian GNN:
-
node_repr
: representations of each node. -
adj_list
: an adjacency list, of which each rowi
consists of the neighbor IDs of nodei
.adj_list
is padded using 0 to make each row of the same size. -
weight
: a weight list for weighted graphs, of which each rowi
contains the weights of neighbors.weight
is padded using 0 to make each row of the same size. -
mask
: thei
-th row ofmask
is 0 if the nodei
is padded. Otherwise, thei
-th row is 1.
Directory
-
dataset
: dataset files. -
gnn
: Riemannian graph neural network implementation. -
hyperbolic_module
: centroid-based classification and Poincaré distance. -
manifold
: Poincaré, Lorentz and Euclidean manifolds. -
optimizer
: Riemannian SGD and Riemannian AMSGrad. -
params
: parameters for each task. -
task
: task code. -
utils
: utility modules and functions.
Hyperparameters
Some notable hyperparameters are listed here.
-
lr
: learning rate for Euclidean variables. -
lr_hyperbolic
: learning rate for hyperbolic variables. -
optimizer
: optimizer for Euclidean variables. -
hyper_optimizer
: optimizer rate for hyperbolic variables. -
num_centroid
: the number of centroids for centroid-based prediction. -
gnn_layer
: the number of GNN layers. -
embed_size
: the embedding size. -
apply_edge_type
: a boolean value denotes multi-relational or single-relational. -
edge_type
: the number of relations for multi-relational datasets. -
select_manifold
: use the Euclidean, Poincaré or Lorentz manifold. -
activation
: the activation function.
License
HGNN is licensed under Creative Commons-Non Commercial 4.0. See the LICENSE file for details.